{"id":"W2759265425","doi":"10.1109/tvt.2017.2756049","title":"Power Split Strategy Optimization of a Plug-in Parallel Hybrid Electric Vehicle","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Canada Research Chairs","keywords":"Plug-in; Driving cycle; Computation; Dynamic programming; Driving range; Optimal control; Energy management; Electric vehicle; Automotive engineering; State of charge; Power management; Genetic algorithm; Power (physics); Battery (electricity); Range (aeronautics); Engineering; Computer science; Control engineering; Mathematical optimization; Energy (signal processing); Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001110556,0.0002458454,0.0003679059,0.0008730634,0.0001846515,0.00003423257,0.0006209479,0.0003458281,0.00004726868],"category_scores_gemma":[0.00001642341,0.0002652232,0.0001039968,0.0005087644,0.0001808066,0.0001895397,0.000004035746,0.000679214,0.00002493254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001145437,"about_ca_system_score_gemma":0.00003564738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003253658,"about_ca_topic_score_gemma":0.00002090342,"domain_scores_codex":[0.998674,0.00001589361,0.0003739453,0.000314076,0.0001641082,0.0004579549],"domain_scores_gemma":[0.99888,0.00002832795,0.0001042783,0.0008868134,0.00006283779,0.00003779775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002343174,0.0001576935,0.0003742913,0.00003170568,0.00008046201,0.0000668272,0.000007834782,0.9530001,0.01428608,0.001149877,0.00003840203,0.03078332],"study_design_scores_gemma":[0.001067767,0.0003804666,0.001536519,0.00005321633,0.00003477095,0.00005749138,0.00002862984,0.5450714,0.4493868,0.001892362,0.0001066495,0.0003839483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6766857,0.0004040244,0.3207235,0.0002636775,0.0001078654,0.0002567022,0.000008205103,0.0009496686,0.0006006649],"genre_scores_gemma":[0.9973167,0.000635085,0.001836975,0.00001033044,0.000006447221,0.00008897366,0.000001389566,0.00004648719,0.00005760363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4351007,"threshold_uncertainty_score":0.99998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00859213253806415,"score_gpt":0.2196539585611992,"score_spread":0.211061826023135,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}